Debt detection and debt recovery with advanced classification techniques

University of Technology Sydney. Faculty of Engineering and Information Technology.My study is part of an ARC linkage project between University of Technology, Sydney and Centrelink Australia, which aims to applying data mining techniques to optimise the debt detection and debt recovery. A debt indicates an overpayment made by the government to a customer who is not entitled to that payment. In social security, an interaction between a customer and the government department is recorded as an activity. Each customer’s activities happen sequentially along the time, which can be regarded as a sequence. Based on the experience of debt detection experts, there are usually some patterns in the sequence of activities of customers who commit debts. The patterns indicating the customers’ intention to be overpaid can thus be used to discover or predict debt occurrence. The development of debt detection and recovery over sequential transaction data, however, is a challenging problem due to following reasons. (1) The size of transaction data is vast, and the transaction data are being generated continuously as the business goes on. (2) Transaction data are always time stamped by the business system, and the temporal order of the transaction data is highly related to the business logic. (3) The patterns and relationships hidden behind the transaction data may be affected by a lot of factors. They are not only dependent on business domain knowledge, but also subject to seasonal and social factors outside the business. Based on a survey of existing methods on debt detection and recovery, data mining techniques are studied in this thesis to detect and recovery debt in an adaptive and efficient fashion. Firstly, sequence data is used to model the evolvement of customer activities, and the sequential patterns generalize the trends of sequences. For long running sequence classification issues, even if the sequences come from the same source, the sequential patterns may vary from time to time. An adaptive sequential classification model is to be built to make the sequence classification adapt to the sequential pattern variation. The model is applied to 15,931 activity sequences from Centrelink which includes 849,831 activity records. The experimental results show that the proposed adaptive sequence classification framework performs effectively on the continuously arriving data. Secondly, a new technique of sequence classification using both positive and negative patterns is to be studied, which is able to find the relationship between activity sequences and debt occurrences and also the impact of oncoming activities on the debt occurrence. The same dataset is used for the evaluation. The outcome shows if built with the same number of rules, in terms of recall, the classifier built with both positive and negative rules outperforms traditional classifiers with only positive rules under most conditions. Finally, decision trees are to be built in the thesis to model debt recovery and predict the response of customers if contacted by phone. The customer contact strategy driven by the model aims to improve the efficiency of debt recovery process. The model is utilized in a real life pilot project for debt recovery in Centrelink. The pilot result outperforms the traditional random customer selection. In summary, this thesis studies debt detection and debt recovery in social security using data mining techniques. The proposed models are novel and effective, showing potentials in real business

Social security data mining : an Australian case study

University of Technology, Sydney. Faculty of Engineering and Information Technology.Data mining in business applications has become an increasingly recognized and accepted area of enterprise data mining in recent years. In general, while the general principle and methodologies of data mining and machine learning are applicable for any business applications, it is often essential to develop specific theories, tools and systems for mining data in a particular domain such as social security and social welfare business. This necessity has led to the concept of social security and social welfare data mining, the focus of this thesis work. Social security and social welfare business involves almost every citizen’s life at different life periods. It provides fundamental and crucial government services and support to varied populations of specific need. A typical scenario in Australia is that it not only connects one third of our populations, but also associates with many relevant stakeholders, including banking business, taxation and Medicare. Such business engages complicated infrastructure, networks, mechanisms, policies, activities, and transactions. Data mining of such business is a brand new application area in the data mining community. Mining such social welfare business and data is challenging. The challenges come from the unavailable benchmark and experience in the data mining for this particular domain, the complexities of social welfare business and data, the exploration of possible doable tasks, and the implementation of data mining techniques in relation to the business objectives. In this thesis, which adopts a practice-based innovative attitude and focusses on the marriage of social welfare business with data mining, we believe we have realised our objective of providing a systematic and comprehensive overview of the social security and social welfare data mining. The main contributions consist of the following aspects: • As the first work of its kind, to the best of our knowledge, we present an overall picture of social security and social welfare data mining, as a new domain driven data mining application. • We explore the business nature of social security and social welfare, and the characteristics of social security data. • We propose a concept map of social security data mining, catering for main complexities of social welfare business and data, as well as providing opportunities for exploring new research issues in the community. • Several case studies are discussed, which demonstrate the technical development of social security data mining, and the innovative applications of existing data mining techniques. The nature of social welfare is spreading widely across the world in both developed and developing countries. This thesis work therefore is timely and could be of important business and government value for better understanding our people, our policies, our objectives, and for better services of those people of genuine needs

International conference on software engineering and knowledge engineering: Session chair

The Thirtieth International Conference on Software Engineering and Knowledge Engineering (SEKE 2018) will be held at the Hotel Pullman, San Francisco Bay, USA, from July 1 to July 3, 2018. SEKE2018 will also be dedicated in memory of Professor Lofti Zadeh, a great scholar, pioneer and leader in fuzzy sets theory and soft computing. The conference aims at bringing together experts in software engineering and knowledge engineering to discuss on relevant results in either software engineering or knowledge engineering or both. Special emphasis will be put on the transference of methods between both domains. The theme this year is soft computing in software engineering & knowledge engineering. Submission of papers and demos are both welcome